Recently, some works that focus on addressing the domain shift on point clouds in the 3D object detection task have been proposed. SN [1]} utilizes the target object size distribution prior to normalize the object from the source domain in order to close the size-level domain gap. The source-free domain adaptation method SF-UDA\(^{3D}\)[2]}, first scales the input target sequence data and re-scales the predictions, then uses the time consistency to score and refine the pseudo labels. SRDAN [3]} adopts the scale-aware and range-aware feature adversarial alignment manners to match the distribution between source and target domain. MLC-Net [4]} leverages the mean-teacher paradigm with three levels of consistency to facilitate the cross-domain transfer. The self-denoising frameworks, ST3D [5]} and ST3D++ [6]}, employ three strategies (i.e., random object scaling, hybrid quality-aware triplet memory, and curriculum data augmentation), to reduce noise in pseudo label generation. Though achieving promising results, most of the aforementioned domain adaptation methods ignore the target data distribution when training the model with source data.
